Article: Latent profiles of computational thinking in first-year university students in Peru Journal: International Journal of Technology Enhanced Learning (IJTEL) 2025 Vol.17 No.7 pp.1 - 14 Abstract: This study aims to classify computational thinking (CT) among first-year Peruvian university students. A sample of 730 students was analysed, focusing on five key dimensions - abstraction, decomposition, algorithmic thinking, evaluation, and generalisation - using a validated CT evaluation tool and the 'mclust' package in R. Four distinct CT profiles were identified, each highlighting unique strengths and weaknesses: Profile 1 exhibited high levels of CT skills, especially in evaluation and algorithmic thinking; Profile 2 showed moderate levels with a balanced distribution across dimensions; Profile 3 indicated significant weaknesses, particularly in decomposition; and Profile 4 had the lowest overall CT skills. Demographic variations explored through SPSS version 27 revealed significant differences in CT profiles based on the type of secondary school attended, with public school students excelling in abstraction. These findings contribute to the discourse on CT, offering practical guidance for educators to tailor interventions and enhance CT skills among university entrants. Inderscience Publishers - linking academia, business and industry through research

Title: Latent profiles of computational thinking in first-year university students in Peru

Authors: Edgar Marin-Ballon; Freddy Begazo-Zegarra; Fiorella Romero-Gomez

Addresses: Universidad Católica de Santa María, San José Urbanization, Umacollo, Arequipa, Peru ' Department of Mathematics and Statistics, Universidad Católica San Pablo, Campiña Paisajista Urbanization, Arequipa, Peru ' Physical and Formal Sciences and Engineering, Universidad Católica de Santa María, San José Urbanization, Umacollo, Arequipa, Peru

Abstract: This study aims to classify computational thinking (CT) among first-year Peruvian university students. A sample of 730 students was analysed, focusing on five key dimensions - abstraction, decomposition, algorithmic thinking, evaluation, and generalisation - using a validated CT evaluation tool and the 'mclust' package in R. Four distinct CT profiles were identified, each highlighting unique strengths and weaknesses: Profile 1 exhibited high levels of CT skills, especially in evaluation and algorithmic thinking; Profile 2 showed moderate levels with a balanced distribution across dimensions; Profile 3 indicated significant weaknesses, particularly in decomposition; and Profile 4 had the lowest overall CT skills. Demographic variations explored through SPSS version 27 revealed significant differences in CT profiles based on the type of secondary school attended, with public school students excelling in abstraction. These findings contribute to the discourse on CT, offering practical guidance for educators to tailor interventions and enhance CT skills among university entrants.

Keywords: computational thinking; CT; latent profile analysis; LPA; higher education; abstraction.

DOI: 10.1504/IJTEL.2025.150975

International Journal of Technology Enhanced Learning, 2025 Vol.17 No.7, pp.1 - 14

Received: 18 Dec 2024
Accepted: 21 Jun 2025
Published online: 06 Jan 2026 *

Title: Latent profiles of computational thinking in first-year university students in Peru

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